The latest release of diffmah is 0.4.1:
$ conda install diffmah
To install diffmah into your environment from the source code:
$ cd /path/to/root/diffmah
$ python setup.py install
For a typical development environment in conda:
$ conda create -n diffit python=3.9 numpy numba flake8 pytest jax ipython jupyter matplotlib scipy h5py diffmah
Data for this project can be found at this URL.
The diffmah_halo_populations.ipynb notebook demonstrates how to calculate the MAHs as a function of the diffmah parameters using the calc_halo_history function. This notebook also demonstrates how to use the mc_halo_population function to generate Monte Carlo realizations of cosmologically representative populations of halos.
The diffmah_fitter_demo.ipynb notebook demonstrates how to fit the MAH of a simulated halo with a diffmah approximation.
See history_fitting_script.py for an example of how to fit the MAHs of a large number of simulated halos in parallel with mpi4py.
The diffmah paper has been published by the Open Journal of Astrophysics. Citation information for the paper can be found at this ADS link, copied below for convenience:
@ARTICLE{2021OJAp....4E...7H,
author = {{Hearin}, Andrew P. and {Chaves-Montero}, Jon{\'a}s and {Becker}, Mathew R. and {Alarcon}, Alex},
title = "{A Differentiable Model of the Assembly of Individual and Populations of Dark Matter Halos}",
journal = {The Open Journal of Astrophysics},
keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies},
year = 2021,
month = jul,
volume = {4},
number = {1},
eid = {7},
pages = {7},
doi = {10.21105/astro.2105.05859},
archivePrefix = {arXiv},
eprint = {2105.05859},
primaryClass = {astro-ph.CO},
adsurl = {https://ui.adsabs.harvard.edu/abs/2021OJAp....4E...7H},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}